Abstract

Subjective video quality assessment (VQA) strongly
depends on semantics, context, and the types of visual distortions.
Currently, all existing VQA databases include only a small number
of video sequences with artificial distortions. The development
and evaluation of objective quality assessment methods would
benefit from having larger datasets of real-world video sequences
with corresponding subjective mean opinion scores (MOS), in
particular for deep learning purposes. In addition, the training
and validation of any VQA method intended to be ‘general
purpose’ requires a large dataset of video sequences that are
representative of the whole spectrum of available video content
and all types of distortions. We report our work on KoNViD-1k, a
subjectively annotated VQA database consisting of 1,200 publicdomain
video sequences, fairly sampled from a large public video
dataset, YFCC100m. We present the challenges and choices we
have made in creating such a database aimed at ‘in the wild’
authentic distortions, depicting a wide variety of content.